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dataset_info:
features:
- name: image
dtype: image
- name: variant
dtype: string
splits:
- name: train
num_bytes: 326073180
num_examples: 70
download_size: 326080167
dataset_size: 326073180
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
size_categories:
- n<1K
---
# Deepcanola Br11
This dataset contains unlabeled RGB images of canola (Brassica napus) plants in field environments, featuring a mix of real field captures and synthetic data. It provides a diverse resource for agricultural computer vision research focused on crop segmentation tasks within natural farming settings. The dataset contains 70 images with no classification, segmentation, or bounding-box annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{vanvliet2025deepcanola,
title={DeepCanola: Phenotyping brassica pods using semi-synthetic data and active learning},
author={van Vliet, Larissa J.J. and Atkins, Kieran and Kurup, Smita and Siles, Laura and Hepworth, Jo and Corke, Fiona M.K. and Doonan, John H. and Lu, Chuan},
journal={Computers and Electronics in Agriculture},
volume={237},
pages={110470},
year={2025},
publisher={Elsevier}
}
```
Atkins, K., van Vliet, L., Doonan, J., & Lu, C. (2024). DeepCanola: Phenotyping Brassica Pods Using Semi-Synthetic Data and Active Learning [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14235543
*This dataset was reformatted from its original format to match HuggingFace standards.*
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